A small neural net simulates coherence and short-term memory in an insect olfactory system

We present a simple neural network model which simulates the experimental a ction potentials measured by Laurent and coworkers from single local (LN) a nd projection neurons (PN) in the olfactory system of an insect, the locust . Our recurrent network consists of one LN and 80 PNs where the individual units (neurons) are described by the Hodgkin-Huxley model. Bifurcation diag rams for the isolated neurons are calculated, where the PNs are oscillatory whereas the LN is treated as a non-oscillatory steady state neuron. The PN -PN and PN-LN synapses are excitatory. Inhibitory synaptic coupling between the LN and all 80 PNs causes all PNs to fire coherently generating a local field potential which precedes the LN by a small phase-shift. The LN and t he PNs receive a scaled antennal nerve current from the olfactory receptor neurons (ORNs) where the receptors bind odor molecules with specific bindin g constants in a simple "open" binding process. We assume, that the odor-bo und receptors exist in two states; an active state (R-1) and an inactive st ate (R-2) leading to adaptation where R-1 is assumed to be proportional to the antennal nerve current. All synaptic strengths are augmented by small i ncrements for each successive odor presentation. Thus, the short-term memor y effect which has been measured by Stopfer and Laurent (M. Stopfer and G. Laurent, Nature, 1999, 402, 664) in 10 repeated presentations of the same o dor, is successfully simulated: the PN action potentials decrease in intens ity, successive signatures simplify and the PN-coherence increases. High PN -frequencies (>50 Hz) abolish the coherence in the range 20-50 Hz. A previo usly augmented synaptic strength is retained after 10 trials and a 30 s res ting period to produce coherence in a "naive" part of the antenna in a subs equent trial.